Applied AI/ML Modeling - Senior Associate

JPMorgan Chase; New York (NY); On-site; USD 150,000 - 230,000; Full time

Job summary

JPMorgan Chase in New York seeks an AI/ML Modeling Specialist to develop and launch models addressing complex Consumer Banking problems, focusing on sales effectiveness and banker enablement. You will manage modeling engagements end-to-end, define success metrics, work with large datasets, and translate outputs into actionable recommendations for non-technical partners to drive adoption.

Collaboration with governance, risk, and controls teams ensures compliant reviews, documented model intent,

Qualifications

• Advanced degree in a quantitative discipline such as Computer Science, Statistics, Machine Learning, Econometrics, Operations Research, Applied Mathematics, or related field.
• 3+ years of hands-on industry experience developing and deploying AI/ML models in production, including statistical modeling and modern ML.
• Proficient in Python with hands-on experience in ML/deep learning frameworks (TensorFlow, PyTorch) and core libraries (NumPy, Scikit-Learn, Pandas).
• Strong working knowledge of notebooks and cloud-based development/compute.
• Deep expertise in at least one of: recommendation/decisioning systems or causal inference and uplift modeling or online learning methods or behavioral modeling and human-in-the-loop systems.
• Ability to communicate complex modeling concepts clearly to non-technical stakeholders and drive decisions.

Responsibilities

• Develop and launch AI/ML models that solve complex business problems in Consumer Banking, focusing on sales effectiveness and banker enablement.
• Participate in end-to-end modeling engagements, scoping use cases, defining success metrics, and working with large datasets to formulate hypotheses.
• Translate model outputs into clear, actionable recommendations for non-technical partners and produce narratives that drive adoption.
• Partner with governance, risk, and controls teams to expedite model reviews, document model intent and limitations, monitor performance and drift, and maintain adherence to model risk management standards.

Skills

Python

TensorFlow

PyTorch

NumPy

Pandas

Scikit-Learn

Data analysis

Communication

Model risk management

Education

Master's degree in CS/Statistics/ML/OR/Applied Math

PhD in a quantitative discipline

Tools

Databricks

Snowflake

Vowpal Wabbit

RLlib

Stable Baselines

Job description

Job responsibilities

• Develop and launch AI/ML models that solve complex, ambiguous business problems in Consumer Banking, with emphasis on s ales effectiveness and banker enablement (e.g., lead scoring, propensity modeling, next-best-action/next-best-offer, customer prioritization, retention, and cross-sell) using techniques such as deep learning, causal inference, contextual bandits, reinforcement learning, and constrained optimization.
• Participate in modeling engagements end-to-end, including scoping use cases with business partners, defining success metrics (incrementality, ROI, adoption), building project plans, and working with large, complex datasets to formulate testable hypotheses.
• Translate model outputs into clear, actionable recommendations for non-technical partners, and produce narratives that drive adoption (why this lead, why now, what action, expected outcome).
• Partner with governance, risk, and controls teams to expedite fair and thorough model reviews, document model intent and limitations, monitor performance and drift, and maintain adherence to regulatory and model risk management standards.

Required qualifications, capabilities, and skills

• Advanced degree (Master's or Ph.D.) in a quantitative discipline such as Computer Science, Statistics, Machine Learning, Econometrics, Operations Research, Applied Mathematics, or a related field.
• 3+ years of hands-on, relevant industry experience developing and deploying AI/ML models in production, including statistical modeling and modern Machine Learning.
• Proficient in Python with hands-on experience in ML/deep learning frameworks (TensorFlow, PyTorch) and core libraries (NumPy, Scikit-Learn, Pandas). Strong working knowledge of notebooks and cloud-based development/compute.
• Deep expertise in at least one of the following, with meaningful exposure to at least one other:
• Recommendation/decisioning systems (next-best-action/offer), ranking, and constrained optimization
• Causal inference and uplift / treatment effect modeling for targeted interventions
• Online learning approaches (contextual bandits, multi-armed bandits, reinforcement learning)
• Behavioral modeling and human-in-the-loop systems that drive adoption and performance
• Demonstrated ability to communicate complex modeling concepts clearly to non-technical stakeholders and drive decisions.

Preferred qualifications, capabilities, and skills

• Ph.D. in a relevant discipline.
• Experience developing advanced AI/ML models in consumer finance, fintech, retail, marketplaces, or other high-scale customer engagement environments.
• Experience with at least one of the following:
• Decisioning/online learning libraries (e.g., Vowpal Wabbit, RLlib, Stable Baselines) or large-scale ranking/recommendation tooling
• Causal inference tooling and experimentation platforms (A/B testing, CUPED, synthetic controls, causal forests, doubly robust methods)
• Familiarity with behavioral science concepts (choice architecture, friction, habit formation) and designing interventions that are effective and compliant.
• Experience with Databricks, Snowflake, or similar platforms; strong practical MLOps experience (model deployment patterns, monitoring, drift detection, retraining, and reproducibility).